Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - When sensor meets tensor
T2 - 25th ACM International Conference on Information and Knowledge Management, CIKM 2016
AU - Ruan, Wenjie
AU - Xu, Peipei
AU - Sheng, Quan Z.
AU - Tran, Nguyen Khoi
AU - Falkner, Nickolas J.G.
AU - Li, Xue
AU - Zhang, Wei Emma
PY - 2016/10/24
Y1 - 2016/10/24
N2 - In the era of the Internet of Things, enormous number of sensors have been deployed in different locations, generating massive time-series sensory data with geo-tags. However, such sensory readings are easily missing due to various reasons such as the hardware malfunction, connection errors, and data corruption. This paper focuses on this challenge-how to accurately yet efficiently recover the missing values for corrupted time-series sensor data with geo-stamps. In this paper, we formulate the time-series sensor data as a 3-order tensor that naturally preserves sensors' temporal and spatial dependencies. Then we exploit its low-rank and sparse-noise structures by drawing upon recent advances in Robust Principal Component Analysis (RPCA) and tensor completion theory. The main novelty of this paper lies in that, we design a highly efficient optimization method that combines the alternating direction method of multipliers and accelerated proximal gradient to recover the data tensor. Besides testing our method using the synthetic data, we also design a real-world testbed by passive RFID (Radio-Frequency IDentification) sensors. The results demonstrate the effectiveness and accuracy of our approach.
AB - In the era of the Internet of Things, enormous number of sensors have been deployed in different locations, generating massive time-series sensory data with geo-tags. However, such sensory readings are easily missing due to various reasons such as the hardware malfunction, connection errors, and data corruption. This paper focuses on this challenge-how to accurately yet efficiently recover the missing values for corrupted time-series sensor data with geo-stamps. In this paper, we formulate the time-series sensor data as a 3-order tensor that naturally preserves sensors' temporal and spatial dependencies. Then we exploit its low-rank and sparse-noise structures by drawing upon recent advances in Robust Principal Component Analysis (RPCA) and tensor completion theory. The main novelty of this paper lies in that, we design a highly efficient optimization method that combines the alternating direction method of multipliers and accelerated proximal gradient to recover the data tensor. Besides testing our method using the synthetic data, we also design a real-world testbed by passive RFID (Radio-Frequency IDentification) sensors. The results demonstrate the effectiveness and accuracy of our approach.
U2 - 10.1145/2983323.2983900
DO - 10.1145/2983323.2983900
M3 - Conference contribution/Paper
AN - SCOPUS:84996490249
SP - 2025
EP - 2028
BT - CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery (ACM)
Y2 - 24 October 2016 through 28 October 2016
ER -